Esther Ulitzsch
Biographic Data
| ID | 8867072 |
|---|---|
| NAME | Esther Ulitzsch |
| GIVEN NAMES | Esther |
| FAMILY NAME | Ulitzsch |
| SIGNATURE | ULITZSCH E |
| AFFILIATIONS | University of Oslo |
| ORCID | 0000-0002-9267-8542 |
| VERIFIED | Yes |
| TOTAL WORKS | 12 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 12 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Bayesian Hierarchical Moderated Factor Analysis for Testing Measurement Invariance in Multilevel Data
Faking in High-Stakes Personality Assessments
When personality assessments are employed in high-stakes contexts, there is the risk that test-takers provide overly positive descriptions of themselves. This response bias is known as faking and has often been addressed in latent variable models through an additional dimension capturing each test-taker’s faking degree. Such models typically assume a homogeneous response strategy for all test-takers, with substantive traits and faking jointly inf…
Conditional Dependencies Between Response Time and Item Discrimination
The use of process data, such as response time (RT) in psychometrics, has generally focused on the relationship between speed and accuracy. The potential relationships between RT and item discrimination remain less explored. In this study, we propose a model for simultaneously estimating the relationships between RT and item discrimination at the person, item, and person-by-item (residual) levels and illustrate our approach through an item-level …
Evaluating Model Predictive Performance in Confirmatory Factor Analysis with Binary Outcomes Using the InterModel Vigorish
Confirmatory Factor Analysis (CFA) has been widely used to assess the fit of theoretical measurement models to observed data. We introduce the InterModel Vigorish (IMV) to the field; a predictive fit index that offers novel perspectives for model comparison. The IMV complements traditional fit indices by offering additional information to support model evaluation, with a particular emphasis on a model's generalizability to the hold-out data. It a…
Assessing the Speed–Accuracy Tradeoff in Psychological Testing Using Experimental Manipulations
The speed–accuracy tradeoff (SAT), where increased response speed often leads to decreased accuracy, is well established in experimental psychology. However, its implications for psychological assessments, especially in high-stakes settings, remain less understood. This study presents an experimental approach to investigate the SAT within a high-stakes spatial ability assessment. By manipulating instructions in a within-subjects design to induce …
Accounting for Measurement Invariance Violations in Careless Responding Detection in Intensive Longitudinal Data
Intensive longitudinal data (ILD) collection methods like experience sampling methodology can place significant burdens on participants, potentially resulting in careless responding, such as random responding. Such behavior can undermine the validity of any inferences drawn from the data if not properly identified and addressed. Recently, a confirmatory mixture model (here referred to as fully constrained latent Markov factor analysis, LMFA) has …
A Model-Based Approach to the Disentanglement and Differential Treatment of Engaged and Disengaged Item Omissions
Item omissions in large-scale assessments may occur for various reasons, ranging from disengagement to not being capable of solving the item and giving up. Current response-time-based classification approaches allow researchers to implement different treatments of item omissions presumably going back to different mechanisms. These approaches, however, are limited in that they require a clear-cut decision on the underlying missingness mechanism an…
The role of rapid guessing and test‐taking persistence in modelling test‐taking engagement
A Bayesian Approach to Estimating Reciprocal Effects with the Bivariate STARTS Model
The bivariate Stable Trait, AutoRegressive Trait, and State (STARTS) model provides a general approach for estimating reciprocal effects between constructs over time. However, previous research has shown that this model is difficult to estimate using the maximum likelihood (ML) method (e.g., nonconvergence). In this article, we introduce a Bayesian approach for estimating the bivariate STARTS model and implement it in the software Stan. We discus…
A Multilevel Mixture IRT Framework for Modeling Response Times as Predictors or Indicators of Response Engagement in IRT Models
Disengaged item responses pose a threat to the validity of the results provided by large-scale assessments. Several procedures for identifying disengaged responses on the basis of observed response times have been suggested, and item response theory (IRT) models for response engagement have been proposed. We outline that response time-based procedures for classifying response engagement and IRT models for response engagement are based on common i…
A Multiprocess Item Response Model for Not-Reached Items due to Time Limits and Quitting
So far, modeling approaches for not-reached items have considered one single underlying process. However, missing values at the end of a test can occur for a variety of reasons. On the one hand, examinees may not reach the end of a test due to time limits and lack of working speed. On the other hand, examinees may not attempt all items and quit responding due to, for example, fatigue or lack of motivation. We use response times retrieved from com…
Using Response Times for Joint Modeling of Response and Omission Behavior
For adequate modeling of missing responses, a thorough understanding of the nonresponse mechanisms is vital. As a large number of major testing programs are in the process or already have been moving to computer-based assessment, a rich body of additional data on examinee behavior becomes easily accessible. These additional data may contain valuable information on the processes associated with nonresponse. Bringing together research on item omiss…
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A Multiprocess Item Response Model for Not-Reached Items due to Time Limits and Quitting
So far, modeling approaches for not-reached items have considered one single underlying process. However, missing values at the end of a test can occur for a variety of reasons. On the one hand, examinees may not reach the end of a test due to time limits and lack of working speed. On the other hand, examinees may not attempt all items and quit responding due to, for example, fatigue or lack of motivation. We use response times retrieved from com…
Using Response Times for Joint Modeling of Response and Omission Behavior
For adequate modeling of missing responses, a thorough understanding of the nonresponse mechanisms is vital. As a large number of major testing programs are in the process or already have been moving to computer-based assessment, a rich body of additional data on examinee behavior becomes easily accessible. These additional data may contain valuable information on the processes associated with nonresponse. Bringing together research on item omiss…
A Multilevel Mixture IRT Framework for Modeling Response Times as Predictors or Indicators of Response Engagement in IRT Models
Disengaged item responses pose a threat to the validity of the results provided by large-scale assessments. Several procedures for identifying disengaged responses on the basis of observed response times have been suggested, and item response theory (IRT) models for response engagement have been proposed. We outline that response time-based procedures for classifying response engagement and IRT models for response engagement are based on common i…
The role of rapid guessing and test‐taking persistence in modelling test‐taking engagement
A Bayesian Approach to Estimating Reciprocal Effects with the Bivariate STARTS Model
The bivariate Stable Trait, AutoRegressive Trait, and State (STARTS) model provides a general approach for estimating reciprocal effects between constructs over time. However, previous research has shown that this model is difficult to estimate using the maximum likelihood (ML) method (e.g., nonconvergence). In this article, we introduce a Bayesian approach for estimating the bivariate STARTS model and implement it in the software Stan. We discus…
A Model-Based Approach to the Disentanglement and Differential Treatment of Engaged and Disengaged Item Omissions
Item omissions in large-scale assessments may occur for various reasons, ranging from disengagement to not being capable of solving the item and giving up. Current response-time-based classification approaches allow researchers to implement different treatments of item omissions presumably going back to different mechanisms. These approaches, however, are limited in that they require a clear-cut decision on the underlying missingness mechanism an…
Assessing the Speed–Accuracy Tradeoff in Psychological Testing Using Experimental Manipulations
The speed–accuracy tradeoff (SAT), where increased response speed often leads to decreased accuracy, is well established in experimental psychology. However, its implications for psychological assessments, especially in high-stakes settings, remain less understood. This study presents an experimental approach to investigate the SAT within a high-stakes spatial ability assessment. By manipulating instructions in a within-subjects design to induce …
Accounting for Measurement Invariance Violations in Careless Responding Detection in Intensive Longitudinal Data
Intensive longitudinal data (ILD) collection methods like experience sampling methodology can place significant burdens on participants, potentially resulting in careless responding, such as random responding. Such behavior can undermine the validity of any inferences drawn from the data if not properly identified and addressed. Recently, a confirmatory mixture model (here referred to as fully constrained latent Markov factor analysis, LMFA) has …
Bayesian Hierarchical Moderated Factor Analysis for Testing Measurement Invariance in Multilevel Data
Faking in High-Stakes Personality Assessments
When personality assessments are employed in high-stakes contexts, there is the risk that test-takers provide overly positive descriptions of themselves. This response bias is known as faking and has often been addressed in latent variable models through an additional dimension capturing each test-taker’s faking degree. Such models typically assume a homogeneous response strategy for all test-takers, with substantive traits and faking jointly inf…
Conditional Dependencies Between Response Time and Item Discrimination
The use of process data, such as response time (RT) in psychometrics, has generally focused on the relationship between speed and accuracy. The potential relationships between RT and item discrimination remain less explored. In this study, we propose a model for simultaneously estimating the relationships between RT and item discrimination at the person, item, and person-by-item (residual) levels and illustrate our approach through an item-level …
Evaluating Model Predictive Performance in Confirmatory Factor Analysis with Binary Outcomes Using the InterModel Vigorish
Confirmatory Factor Analysis (CFA) has been widely used to assess the fit of theoretical measurement models to observed data. We introduce the InterModel Vigorish (IMV) to the field; a predictive fit index that offers novel perspectives for model comparison. The IMV complements traditional fit indices by offering additional information to support model evaluation, with a particular emphasis on a model's generalizability to the hold-out data. It a…
Item response theory (8 works) · Computer Science (7 works) · Psychology (7 works) · Psychometric Methodologies and Testing (7 works) · Mathematics (6 works) · Psychometrics (6 works) · Statistics (6 works) · Econometrics (5 works) · Cognitive Abilities and Testing (4 works) · Cognitive psychology (4 works)